Comparative analysis of approaches necessary for designing a robotic module for industrial parts defect detection

Abstract

Over the past two decades, the most frequently implemented method of oil extraction in the Russian Federation has been a mechanized method using an electric submersible pump (ESP) unit. Due to the remoteness of the fields from the base of repair enterprises, the cost of transporting spent but maintainable ESPs significantly exceeds their purchase price, which over time leads to the formation of a significant amount of decommissioned oil production equipment. To solve this problem, it is proposed to develop a mobile robotic module for sorting, defecting and storing pump parts, thanks to its use, minor repairs of equipment will be possible directly at production sites. The article deals with the problems of complex flaw detection of metal and non-metallic parts with the possibility of applying the developed methodology for a wide range of industrial products, at the same time, the main attention is paid to work with ESP parts. Based on the decomposition of the problem, the most problematic operations were identified: classification of parts, surface control (identification of defects), dimensional control. The results of a brief comparative analysis for each of the above subtasks are based on a review of the scientific literature over the past 30 years, with the largest number of sources reviewed in the last 5 years. As a result optimal methods for solving the task were derived — a machine learning technique for classifying surface defects, the use of a coordinate measuring machine with a manipulator for dimensional control. A new approach is also proposed to solve the main problem of machine learning methods (lack of training samples) in the form of using synthetic photorealistic images for classification with transfer of defect features from semantically close and publicly available training samples.